Distributed platform for computation and trusted validation

ABSTRACT

An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

TECHNICAL FIELD

This application generally relates to a simulation and learning system,and more particularly, to a decentralized database such as a blockchainin which an evolving computation state is stored on a blockchain wheremultiple nodes share in a trusted validation and verification of thecomputation.

BACKGROUND

A centralized database stores and maintains data in one single database(e.g., database server) at one location. This location is often acentral computer, for example, a desktop central processing unit (CPU),a server CPU, or a mainframe computer. Information stored on acentralized database is typically accessible from multiple differentpoints. Multiple users or client workstations can work simultaneously onthe centralized database, for example, based on a client/serverconfiguration. Because of its single location, a centralized database iseasy to manage, maintain, and control, especially for purposes ofsecurity. Within a centralized database data integrity is maximized anddata redundancy is minimized as a single storing place of all data alsoimplies that a given set of data only has one primary record. This aidsin maintaining data as accurate and as consistently as possible andenhances data reliability.

However, a centralized database suffers from significant drawbacks. Forexample, a centralized database has a single point of failure. Inparticular, if there is no fault-tolerance setup and a hardware failureoccurs, all data within the database is lost and work of all users isinterrupted. In addition, a centralized database is highly dependent onnetwork connectivity. As a result, the slower the Internet connection,the longer the amount of time needed for each database access. Anotherdrawback is that bottlenecks occur when the centralized databaseexperiences high traffic due to the single location. Furthermore, acentralized database provides limited access to data because only onecopy of the data is maintained by the database. As a result, multipleusers may not be able to access the same piece of data at the same timewithout creating problems such as overwriting stored data. Furthermore,because a central database system has minimal to no data redundancy, ifa set of data is unexpectedly lost it is difficult to retrieve it otherthan through manual operation from back-up disk storage.

A decentralized database such as a blockchain system provides a storagesystem capable of addressing the drawbacks of a centralized database. Ina blockchain system, multiple peer nodes store a distributed ledger.Clients interact with peer nodes to gain access to the blockchain. Thepeer nodes may be controlled by different entities having differentinterests and therefore are not trusting entities with respect to oneanother. Furthermore, there is no central authority in a blockchain.Therefore, in order for data to be added to or changed on thedistributed ledger in a trusted manner, a consensus of peer nodes mustoccur. The consensus provides a way for trust to be achieved in ablockchain system of untrusting peer nodes.

Meanwhile, computation and learning systems are increasingly beingdeployed in a variety of environments to understand, infer, and mostimportantly, make decisions at a policy-based scale for scenarios withlarge-scale implications. For instance, a simulation environment may beused to understand a propagation of a disease, and the effects ofvarious control mechanisms in varying geographical and demographiccontexts. Similarly, deep learning may be used to learn underlyingstates of systems and are increasingly deployed in day-to-dayapplications such as self-driving cars, analytics, supply chain, andmany others.

As another example, computations associated with fields such asepidemiology and meteorology may take weeks of running time. Forexample, malaria data scientist (MDS) may run experiments to simulatedisease spread using two or more computational model agents (e.g.,OpenMalaria and Epidemiological Modeling software (EMOD)). The modelruns many distinct simulations to compare effects of changes in modelstructure or parameters. The goal of the experiments is to come up withvalidated policies and intervention strategies to eradicate diseases.

These large-simulation based systems often require inputs and outputs tobe shared among multiple disparate parties for timely decision making ina low-resource, and social good context. However, much of thesecomputations and inferences (e.g., training of a deep neural networksetc.) are made locally by individual, and often independent agents,resulting in a system of untrusted interacting systems. Considering thescale of impact that such systems have, it is important that the resultsinferred by individual agents are trustworthy and transferable.Furthermore, endorsers can be erroneous, adversarial, or slow tocompute. Therefore, validation can be expensive in terms of time toendorse each frame. For example, if an endorser is a straggler, thenobtaining endorsement can be unduly delayed. Therefore, it is essentialthat entities in the system can trust the validity of the conclusionsobtained by individual agents and that such validity is performedefficiently, to use them for further study and development as a group.Transparency also guarantees provenance in such systems wherein it isimportant to be able to identify the source of errors.

SUMMARY

One example embodiment may provide a computing system that includes oneor more of a processor configured to one or more of receive data of asimulation, identify checkpoints within the simulation data, andgenerate a plurality of sequential data structures based on theidentified checkpoints, where each data structure identifies of anevolution of a state of the simulation with respect to a previous datastructure among the sequential data structures, and a network interfaceconfigured to transmit the sequential data structures to nodes of ablockchain network for inclusion in one or more data blocks within ahash-linked chain of data blocks.

Another example embodiment may provide a method that includes one ormore of obtaining data of a simulation, identifying checkpoints withinthe simulation data, generating a plurality of sequential datastructures based on the identified checkpoints, where each datastructure identifies an evolving state of the simulation with respect toa previous data structure among the sequential data structures, andtransmitting the generated sequential data structures to nodes of ablockchain network for inclusion in one or more data blocks within ahash-linked chain of data blocks.

A further example embodiment may provide a non-transitory computerreadable medium comprising instructions, that when read by a processor,cause the processor to perform one or more of obtaining data of asimulation, identifying checkpoints within the simulation data,generating a plurality of sequential data structures based on theidentified checkpoints, where each data structure identifies an evolvingstate of the simulation with respect to a previous data structure amongthe sequential data structures, and transmitting the generatedsequential data structures to nodes of a blockchain network forinclusion in one or more data blocks within a hash-linked chain of datablocks.

Another example embodiment may provide a computing system that includesone or more of a network interface configured to receive a datastructure from among sequential data structures of an iterativesimulation that store state data of the iterative simulation, and aprocessor configured to one or more of generate a local computation ofstate data for the data structure based on previously validated statedata from a previous data structure, and determine a similarity betweenthe local computation of state data and the state data within thereceived data structure, wherein, in response to a determination thatthe similarity is within a predetermined threshold, the processor may befurther configured to control the network interface to transmit anendorsement of the data structure to a blockchain network for inclusionwithin a data block among a hash-linked chain of data blocks.

Another example embodiment may provide a method that includes one ormore of receiving a data structure from among sequential data structuresof an iterative simulation, the data structure storing state data of theiterative simulation, generating a local computation of state data forthe data structure based on previously validated state data from aprevious data structure, determining a similarity between the localcomputation of state data and the state data within the received datastructure, and in response to a determination that the similarity iswithin a predetermined threshold, transmitting an endorsement of thedata structure to a blockchain network for inclusion within a data blockamong a hash-linked chain of data blocks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a blockchain network for validating andstoring simulation data, according to example embodiments.

FIG. 2A is a diagram illustrating a peer node blockchain architectureconfiguration for an asset sharing scenario, according to exampleembodiments.

FIG. 2B is a diagram illustrating a communication sequence between nodesof a blockchain network, according to example embodiments.

FIG. 3 is a diagram illustrating a permissioned blockchain network,according to example embodiments.

FIG. 4A is a diagram illustrating a process of a client node generatingframes of simulation data, according to example embodiments.

FIG. 4B is a diagram illustrating a process of an endorsing nodevalidating a frame of simulation data, according to example embodiments.

FIG. 4C is a diagram illustrating a process of an ordering nodearranging frames of simulation data in blocks, according to exampleembodiments.

FIG. 5A is a diagram illustrating a method of generating frames ofsimulation data for storage on a blockchain, according to exampleembodiments.

FIG. 5B is a diagram illustrating a method of endorsing a frame ofsimulation data, according to example embodiments.

FIG. 5C is a diagram illustrating a method of assigning successiveframes of simulation data for parallel endorsement, according to exampleembodiments.

FIG. 5D is a diagram illustrating a method of ordering simulation dataas a result of parallel endorsement processing, according to exampleembodiments.

FIG. 5E is a diagram illustrating a method of compressing simulationcontent, according to example embodiments.

FIG. 5F is a diagram illustrating a method of endorsing simulationcontent, according to example embodiments.

FIG. 6A is a diagram illustrating a physical infrastructure configuredto perform various operations on the blockchain in accordance with oneor more operations described herein, according to example embodiments.

FIG. 6B is a diagram illustrating a smart contract configuration amongcontracting parties and a mediating server configured to enforce smartcontract terms on a blockchain, according to example embodiments.

FIG. 6C is a diagram illustrating a smart contract configuration amongcontracting parties and a mediating server configured to enforce thesmart contract terms on the blockchain according to example embodiments.

FIG. 6D is a diagram illustrating another example blockchain-based smartcontact system, according to example embodiments.

FIG. 7A is a diagram illustrating a process of a new block being addedto a blockchain ledger, according to example embodiments.

FIG. 7B is a diagram illustrating contents of a data block structure forblockchain, according to example embodiments.

FIG. 8 is a diagram illustrating an example computer system configuredto support one or more of the example embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Thus, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, the usage of the phrases “exampleembodiments”, “some embodiments”, or other similar language, throughoutthis specification refers to the fact that a particular feature,structure, or characteristic described in connection with the embodimentmay be included in at least one embodiment. Thus, appearances of thephrases “example embodiments”, “in some embodiments”, “in otherembodiments”, or other similar language, throughout this specificationdo not necessarily all refer to the same group of embodiments, and thedescribed features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

In addition, while the term “message” may have been used in thedescription of embodiments, the application may be applied to many typesof network data, such as, packet, frame, datagram, etc. The term“message” also includes packet, frame, datagram, and any equivalentsthereof. Furthermore, while certain types of messages and signaling maybe depicted in exemplary embodiments they are not limited to a certaintype of message, and the application is not limited to a certain type ofsignaling.

Large-scale computational experiments may be used to make policy-leveldecisions by establishing machine learning models, deep learningsystems, predictive analytics, and the like. These computations areoften derived from the fusion of individual research and development ofmultiple independent organizations. However, these organizations are notnecessarily trusting of one another. Furthermore, each organizationtypically maintains its own local version of the model and data whichcan create shifts in the model between different agencies. The exampleembodiments provide methods, systems, non-transitory computer readablemedia, devices, and/or networks, which provide a blockchain networkwhich supports a distributed computation environment that establishestrust among nodes which may be associated with different organizationsand entities. The system described herein also establishes provenancesuch that each entity is able to track source of decisions. The systemalso provides accountability and transparency which are critical tomulti-agent systems such as distributed computing and system of systemscontexts. The system ensures validation of the computation byguaranteeing consistent local computations, agreed upon as beingcorrect, and verification of the computation by tracking computationconsistency through recorded audits.

A decentralized database is a distributed storage system which includesmultiple nodes that communicate with each other. A blockchain is anexample of a decentralized database which includes an append-onlyimmutable data structure resembling a distributed ledger capable ofmaintaining records between mutually untrusted parties. The untrustedparties are referred to herein as peers or peer nodes. Each peermaintains a copy of the database records and no single peer can modifythe database records without a consensus being reached among thedistributed peers. For example, the peers may execute a consensusprotocol to validate blockchain storage transactions, group the storagetransactions into blocks, and build a hash chain over the blocks. Thisprocess forms the ledger by ordering the storage transactions, as isnecessary, for consistency. In a public or permission-less blockchain,anyone can participate without a specific identity. Public blockchainsoften involve native cryptocurrency and use consensus based on a proofof work (PoW). On the other hand, a permissioned blockchain databaseprovides a system which can secure inter-actions among a group ofentities which may share a common goal but which may not fully trust oneanother, such as businesses that exchange funds, goods, information, andthe like.

A blockchain operates arbitrary, programmable logic tailored to adecentralized storage scheme and referred to as “smart contracts” or“chaincodes.” In some cases, specialized chaincodes may exist formanagement functions and parameters which are referred to as systemchaincode. Smart contracts are trusted distributed applications whichleverage tamper-proof properties of the blockchain database and anunderlying agreement between nodes which is referred to as anendorsement or endorsement policy. In general, blockchain transactionstypically must be “endorsed” before being committed to the blockchainwhile transactions which are not endorsed are disregarded. A typicalendorsement policy allows chaincode to specify endorsers for atransaction in the form of a set of peer nodes that are necessary forendorsement. When a client sends the transaction to the peers specifiedin the endorsement policy, the transaction is executed to validate thetransaction. After validation, the transactions enter an ordering phasein which a consensus protocol is used to produce an ordered sequence ofendorsed transactions grouped into blocks.

Nodes are the communication entities of the blockchain system. A “node”may perform a logical function in the sense that multiple nodes ofdifferent types can run on the same physical server. Nodes are groupedin trust domains and are associated with logical entities that controlthem in various ways. Nodes may include different types, such as aclient or submitting-client node which submits a transaction-invocationto an endorser (e.g., peer), and broadcasts transaction-proposals to anordering service (e.g., ordering node). Another type of node is a peernode which can receive client submitted transactions, commit thetransactions and maintain a state and a copy of the ledger of blockchaintransactions. Peers can also have the role of an endorser, although itis not a requirement. An ordering-service-node or orderer is a noderunning the communication service for all nodes, and which implements adelivery guarantee, such as a broadcast to each of the peer nodes in thesystem when committing transactions and modifying a world state of theblockchain, which is another name for the initial blockchain transactionwhich normally includes control and setup information.

A ledger is a sequenced, tamper-resistant record of all statetransitions of a blockchain. State transitions may result from chaincodeinvocations (i.e., transactions) submitted by participating parties(e.g., client nodes, ordering nodes, endorser nodes, peer nodes, etc.).A transaction may result in a set of asset key-value pairs beingcommitted to the ledger as one or more operands, such as creates,updates, deletes, and the like. The ledger includes a blockchain (alsoreferred to as a chain) which is used to store an immutable, sequencedrecord in blocks. The ledger also includes a state database whichmaintains a current state of the blockchain. There is typically oneledger per channel. Each peer node maintains a copy of the ledger foreach channel of which they are a member.

A chain is a transaction log which is structured as hash-linked blocks,and each block contains a sequence of N transactions where N is equal toor greater than one. The block header includes a hash of the block'stransactions, as well as a hash of the prior block's header. In thisway, all transactions on the ledger may be sequenced andcryptographically linked together. Accordingly, it is not possible totamper with the ledger data without breaking the hash links. A hash of amost recently added blockchain block represents every transaction on thechain that has come before it, making it possible to ensure that allpeer nodes are in a consistent and trusted state. The chain may bestored on a peer node file system (i.e., local, attached storage, cloud,etc.), efficiently supporting the append-only nature of the blockchainworkload.

The current state of the immutable ledger represents the latest valuesfor all keys that are included in the chain transaction log. Because thecurrent state represents the latest key values known to a channel, it issometimes referred to as a world state. Chaincode invocations executetransactions against the current state data of the ledger. To make thesechaincode interactions efficient, the latest values of the keys may bestored in a state database. The state database may be simply an indexedview into the chain's transaction log, it can therefore be regeneratedfrom the chain at any time. The state database may automatically berecovered (or generated if needed) upon peer node startup, and beforetransactions are accepted.

Some benefits of the instant solutions described and depicted hereininclude storing validated states of iterative simulations in frames ofiterations. That is, states are computed over iterations by the client,compressed in frames, and validated by the endorsers. Upon validation,these frames are added to the blockchain sequentially. In the case ofenumerative experiments (non-iterative evaluations of outputs for giveninputs), the blockchain may store validated frames of input-outputpairs. The state of the system, as it evolves over the iterations, maybe stored on the blockchain in frames of iterations. The frames arecharacterized by a checkpoint at the beginning and defined adaptively toinclude states as long as the update (difference) are within a definedtolerance threshold. The maximum size of a frame may be appropriatelychosen, and each frame may include as many states as allowed by thesize. Furthermore, the validation performed by the endorsers isparallelized such that the frames can be validated in parallel. Thenon-iterative experiments can also be parallelized, whereas theiterative simulation is run sequentially by the client.

In addition, the example embodiments also provide framing andcompression framework for overhead reduction. Each frame may becompressed differently based on whether the simulation is iterative ornon-iterative. The system also provides successive refinement ofinvalidated states (which are not endorsed) to correct approximationerror for parameter agnostic design. For the non-iterative simulations,a minimum spanning tree (MST) based frame construction for enumerative(non-iterative) simulations may be performed for compression. The systemalso provides coded computing-based task allocation for validation timereduction, and validation for simulations with external randomness usingprobabilistic validation guarantee.

Blockchain is different from a traditional database in that blockchainis not a central storage but rather a decentralized, immutable, andsecure storage, where nodes must share changes to records in thestorage. Some properties that are inherent in blockchain and which helpimplement the blockchain include, but are not limited to, an immutableledger, smart contracts, security, privacy, decentralization, consensus,endorsement, accessibility, and the like, which are further describedherein. According to various aspects, the validated states of acomputational process via a distributed platform is implemented due toimmutability, security, decentralization, consensus, endorsement, andaccessibility, which are inherent and unique to blockchain.

In particular, because the record of validated states is immutable,verification of the simulation for consistency of the computationstranslates to checking the consistency of the hash chain on theblockchain. This is much easier than running the entire simulation andthus makes verification simple to guarantee. Accountability can beguaranteed by the blockchain because it is feasible to trace not onlythe source of the computations, but also identify errors in thecomputation and their origin. This establishes provenance in thecomputational system.

The system further provides security from adversaries in thecomputation, endorsement, and storage because the states, oncevalidated, can be trusted at any later point in time by referring to theblockchain. This allows for trust that is transferrable across usageinstances of the simulation results. Furthermore, because the entireprocess of validation, computation, and storage is decentralized, thereexists no central point of attack to corrupt the simulation results. Thedistributed nature also allows for a reduction of computational costsand time owing to parallelizability of the process.

Consensus of validations among the peers is critical to enabling trustas it is essential that at least a sufficient fraction of peers acceptthe computations as being correct, and also that the data stored on thecopies of the ledger are up to date across the peers. Furthermore,endorsements of the steps of the computation by independent endorsers inthe network allows for trust in the validation process as the randomassignment of endorsers in the network implies that the effect ofadversarial peers is negated through the redundancy, and the overallnature of the network to function honestly. Because the blockchainledger is distributed, and shared among all peers, any peer who wishesto access the results of the simulation or draw inferences from theiterates is able to do this easily by referring to their local copy ofthe ledger

The example embodiments provide numerous benefits over a traditionaldatabase. For example, the design of the system requires a datastructure that can be shared among the peers in the network in aconsistent manner, is immutable, and can be updated in a distributedmanner. All these translate precisely to a blockchain. Additionally, theblockchain also allows for a simple consistency check that translates toverifiability of the simulation validity. Whereas it may be technicallyfeasible for another data structure to offer these features, there isnot a single unifying mechanism that offers all the above describedrequirements. In this sense, the blockchain proves to be the optimalchoice.

The blockchain may be used in conjunction with a compression schema thatfirst performs a novel compression of the validated states to allow forscalability of the implementation. In the absence of the compressionschema, the storage and communication overheads grow prohibitivelylarge, preventing the scaling of the design to large-scale simulations,and large networks. Furthermore, the validation process defined here isnew when compared to the notions of endorsements as applicable incryptocurrencies and smart contracts, in that it is based on are-computation of a function to test for report deviation, as againstsomething like a check of availability of resources.

The example embodiments also change how data may be stored within ablock structure of the blockchain. For example, related blockchains maystore information such as transactions (transfer of property), and smartcontracts (instruction sets to be executed). Meanwhile, in the exampleembodiments, the data being stored is the validated states of acomputational process. That is, each block consists of one or moreframes that include a compressed checkpoint of a state of thecomputation and quantized state updates obtained from delta encoding ofsuccessive iterates in the frame. Additional metadata that may be storedcan include the ID of the client performing the simulation, theendorsers who validated the states, the iterate index, and otherextraneous information used for performing the simulation. By storingvalidated states of a computation within data blocks of a blockchain,the validated states of the computation may be appended to an immutableledger through a hash-linked chain of blocks and may be used validatesubsequent iterations of the computation as well as perform compression.

Various technical advantages are provided by the example embodimentsdescribed herein. For example, the embodiments provide a distributedvalidation framework for large-scale iterative simulations by reportingstates of the simulation to subsets of endorsers who validate states inparallel. In addition, a compression schema is provided that identifiescheckpoints, constructs frames of states of the simulation and performsdelta encoding and lattice vector quantization to compress the framesbefore dispatching for validation in parallel. In addition, a blockchainledger is provided for storing validated, compressed frames of statesfrom the simulation using an orderer to check for endorsementconsistency. Furthermore, an MST-based frame construction method isprovided to create trees of states to perform compression in enumerativeexperiments. Furthermore, a coded computing-based endorsement allocationis provided to reduce computational time for state validation.

FIG. 1 illustrates a blockchain network 100 for validating and storingsimulation data, according to example embodiments. Referring to FIG. 1 ,the blockchain network 100 includes a client node 110 which may performlarge-scale computation for training a model or which may receivelarge-scale computation data from another system or systems. Thelarge-scale computation may generate simulation data for training amodel/data or for computing a non-iterative process. The simulation datamay be converted into data structures such as frames, data points, orthe like, based on checkpoints within the simulation data and the datastructures may be compressed to reduce overhead.

Checkpoints may be used to identify the next frame, etc. The checkpointsmay be determined by the client node based on iterative data,non-iterative input/output pairs, and the like. The frames may includesuccessive frames of data as the state of the model evolves. Theblockchain network 100 also includes multiple subsets of endorsing nodes120, 130, and 140, which may be mutually exclusive from one another, andwhich may be used to validate frames or other data structures ofsimulation state data in parallel. When a frame of data is validated byone of the endorsing node subsets 120, 130, and/or 140, the frame may besent to orderer 150 for inclusion within a data block among a blockchain160, also referred to herein as a hash-linked chain of blocks.

The simulation may be an experiment that benefits from multiplecomputational nodes. As a non-limiting example, a malaria data scientist(MDS) may run experiments to simulate a spreading of a disease using oneor more computational model agents such as OpenMalaria orEpidemiological Modeling software (EMOD). In this example, the modelruns many distinct simulations to compare effects of changes in modelstructure or parameters. The goal of the experiments is to come up withvalidated policies and intervention strategies to eradicate diseases.The system herein may be used to establish accountability andtransparency in computational results of simulations.

Referring again to FIG. 1 , a distributed validation is performed by thesubsets of endorsing nodes 120, 130, and 140. A consensus ofre-computation of local operations to endorse may be generated by thesubsets and reported computations. Once validated by a subset, thevalidated frame of data may be stored on the blockchain 160 and sharedamong all nodes on a blockchain network which may include client node110, endorsing nodes (subsets 120-140), other peer nodes, and/or thelike. The blockchain 160 creates a shared, immutable, append-only recordof validated frames between successive checkpoints. Furthermore, lossycompression may be used to compress the state data within each framethereby reducing storage cost and computational overhead.

The client node 110 may also be referred to as an agent performingcomputation. The client node 110 may store a state of the model, data,etc., at a checkpoint in the form of a frame. The frame may includecompressed state updates (differences) within the frame and the statedata within a previous frame. The frame may be part of a larger group offrames that are generated as successive frames. The frames may beverified by endorsers (other agents) within the endorsing node subsets120, 130, and 40, by recomputing. The validated frames may be added tothe blockchain 160 which is shared among the endorsers, peers, clients,etc. of the blockchain network.

A state of the system evolves over iterations of the simulation asX _(t+1) =f(X _(t),θ_(t))X_(t)∈

^(d), system state vectorθ_(t)∈

^(d′) is a shared source of (possibly random) informationf:

^(d)×

^(d′)→

^(d), atomic operation, shared by all peersValidity of simulation ⇔ validity of intermediate states {X₁, X₂ . . . }Assume f(⋅) is L-Lipschitz continuous, i.e.,∥f(x ₁)−f(x ₂)∥≤L∥x ₁ −x ₂∥

The simulation may be an iterative evolution of the stateX_(t+1)=f(X_(t),θ_(t)). The client node 110 may report the state X_(t)at checkpoints t∈{T₁, T₂, . . . }. For iterations between checkpoints,t∈(T₁, T₂), report compressed update

. In response, the endorsing subset of nodes may recompute the frame ofdata based on previously validated states reported by the client node110, which are compared with the received frame. Furthermore, theorderer 150 may add a validated frame to the blockchain 160 in asequential manner.

Furthermore, the client node 110 may use lossy compression (similar to avideo encoding process) to report state updates (differences), ∥

_(t)−ΔX_(t)∥≤∈, for small ∈. Here, a frame width (checkpointingfrequency) may be determined adaptively such that ∥ΔX_(t)∥≤Δ_(quant) forall slots in the frame, and a number of iterates in a frame may belimited by a maximum number. The current state may be stored at abeginning of a frame (checkpoint) using Lempel-Ziv-like lossycompressor. Based on rate of compression, costs of communication andstorage vary. If the simulation is a chaotic simulation, the system mayrequire smaller frames and larger compression rates, however,embodiments are not limited thereto.

In the network 100 of FIG. 1 , endorsement parallelization isimplemented because individual frames can be validated by disjointendorser subsets in parallel. Here, every endorser validates only afraction of the total frames generated from the simulation therebysignificantly reducing computational overhead. Furthermore, endorsersneed not wait for peers with large communication delays or poorcomputational power and can focus on the frame they've been tasked withendorsing. When an endorsing subset is unable to validate a frame ofdata, the endorsing subset may notify the client node 110. If confident,the client node 110 can communicate report update using successiverefinement without re-computation. Otherwise, the client node 110 canrecompute the frame and send it to the subset for endorsing, again.

To determine a frame endorsement, an endorsing peer node may determinean acceptable margin of deviation Δ_(marg) for simulation. Accordingly,a computation may only be declared as valid if the state of themodel/simulation reported by the client node 110 is within theacceptable margin of deviation for the recomputed state. The subset ofpeers endorses atomic computations in frame and add to the blockchainupon consensus. Furthermore, margins can be adaptively reduced, andcompression schemes appropriately updated to obtain tighter requirementscloser to termination of simulation (or convergence of state).

The orderer 150 may receive endorsements for different frames from thedifferent subsets of endorsing nodes 120, 130, and 140. The endorser 150may generate or otherwise initialize data blocks for storage on theblockchain 160. Here, the endorser may order the frames sequentially tobe added to the blockchain 160 upon consensus. The stored frames areimmutable and can be used for verifying checkpoints and frames byendorsing nodes. The state updates in a frame can also be sub-sampled bythe orderer 150 and stored for lesser storage cost.

In some embodiments, the computation involves an iterative computationsuch as a refining of a deep learning model, machine learning algorithm,etc. As another example, the experiment could involve a non-iterativesimulation which includes a large set of input parameters using anatomic computational block and generating an output for each input. Inthis example, states of the simulation may be input-output pairs whichprovide no natural grouping into frames making compression difficult.

However, frames may be constructed via a minimum-spanning tree (MST) andsubsequently compressed. Here, constructing a frame of state updatesnecessary for efficient delta encoding and compressive reporting ofstate updates may be performed. In this example, a pairwise distancematrix

=[∥Z_(i)−Z_(j)∥]_(i,j), where Z_(i)=(X_(i),Y_(i)), may be utilized. Theclient node 110 may generate the MST of weighted graph G([n],

). Threshold edge weights by Δ_(quant), and prune trees to include atmost M nodes each. Furthermore, each tree may corresponds to a frame andmay include creating a checkpoint with the root of the tree andcommunicating quantized state updates along edges of tree. Furthermore,the same compression schemes may be used as described for iterativedata.

In the example of non-iterative simulation data, the client node 110 mayreport the compressed frame and the MST structure relating states to anendorsing subset. In response, the endorsing nodes may decompress states({tilde over (X)}_(i),{tilde over (Y)}_(i)). The endorsers may recomputeoutputs from reported inputs: Ŷ_(i)=f({tilde over (X)}_(i)) and validatea state report in a frame if ∥Ŷ_(i)−{tilde over (Y)}_(i)∥≤Δ_(marg).Furthermore, the endorser may validate the frame if all states arevalid. When number of computations (inputs) is large, frameconstruction, local validation, and endorsement parallelization reducescosts and ensures computational validity.

Simulations are typically sensitive to external source of randomness(θ_(t)), i.e., X_(t+1)=f(X_(t),θ_(t)). Without access to randomness,individual endorsers can not recompute the reported state. A possiblesolution, store θ_(t) at each iteration which creates very high storagecost. As another example, validate by testing deviation of reportedstate from average of states recomputed by independent endorsers. Here,if a source of randomness is the same, endorsers collectively behavealong the expected path, for well-behaved functions

The description herein assumes endorser honesty, and computationalhomogeneity for timely validation. However, if endorsers are erroneous,adversarial, or are slow to compute, validation can be expensive interms of time to endorse each frame. For example, if an endorser is astraggler, then obtaining endorsement can be unduly delayed. Fordistributed trust using untrusting peers, there is a need for redundancyin validation by allocation of multiple endorsers for each statevalidation. Accordingly, endorsement policies (such as at least τvalidations necessary) can be defined depending on number of adversarialpeers in the system.

In some embodiments, using coded computing for allocation of states tobe validated by an endorser in a frame, can reduce the number ofcomputations performed by each endorser. In doing so, the system reducesthe time for validation under threshold endorsement policies but usesmore endorsers.

The system described herein provides a number of advantages fortraditional simulation-based systems. For example, accountability isensured because the environment guarantees provenance in design and amethod to track local computations in multi-agent systems. Transparencyis created by trust established among nodes through transparency in theform of regular audits and local computation validation via theblockchain. The frame design, endorsement, and validation can be adaptedaccording to the state evolution. Here, by varying validation margin,trust requirements can be appropriately altered. The platform useselements that are fairly general and can be implemented using any one ofa wide variety of design parameters and algorithms. Furthermore, thedesign is agnostic to specifics of simulation and can be implemented aslong as simulation is decomposable into reproducible atomiccomputations. The system requires fairly simple building blocks tocreate the platform which can be appropriately developed from existingcompression and blockchain technology. Furthermore, by storingintermediate evaluations of system state, the platform guaranteesreliable data and model sharing, and collaborative research.

FIG. 2A illustrates a blockchain architecture configuration 200,according to example embodiments. Referring to FIG. 2A, the blockchainarchitecture 200 may include certain blockchain elements, for example, agroup of blockchain nodes 202. The blockchain nodes 202 may include oneor more nodes 204-210 (four nodes are depicted by example only). Thesenodes participate in a number of activities, such as blockchaintransaction addition (e.g., simulation data frames, etc.) and validationprocesses (consensus). One or more of the blockchain nodes 204-210 mayendorse transactions based on endorsement policy and may provide anordering service for all blockchain nodes in the architecture 200. Ablockchain node may initiate a blockchain authentication and seek towrite to a blockchain immutable ledger stored in blockchain layer 216, acopy of which may also be stored on the underpinning physicalinfrastructure 214. The blockchain configuration may include one or moreapplications 224 which are linked to application programming interfaces(APIs) 222 to access and execute stored program/application code 220(e.g., chaincode, smart contracts, etc.) which can be created accordingto a customized configuration sought by participants and can maintaintheir own state, control their own assets, and receive externalinformation. This can be deployed as a transaction and installed, viaappending to the distributed ledger, on all blockchain nodes 204-210.

The blockchain base or platform 212 may include various layers ofblockchain data, services (e.g., cryptographic trust services, virtualexecution environment, etc.), and underpinning physical computerinfrastructure that may be used to receive and store new transactionsand provide access to auditors which are seeking to access data entries.The blockchain layer 216 may expose an interface that provides access tothe virtual execution environment necessary to process the program codeand engage the physical infrastructure 214. Cryptographic trust services218 may be used to verify transactions such as asset exchangetransactions and keep information private.

The blockchain architecture configuration of FIG. 2A may process andexecute program/application code 220 via one or more interfaces exposed,and services provided, by blockchain platform 212. The code 220 maycontrol blockchain assets. For example, the code 220 can store andtransfer data, and may be executed by nodes 204-210 in the form of asmart contract and associated chaincode with conditions or other codeelements subject to its execution. As a non-limiting example, smartcontracts may be created to execute reminders, updates, and/or othernotifications subject to the changes, updates, etc. The smart contractscan themselves be used to identify rules associated with authorizationand access requirements and usage of the ledger. For example, the readset 226 may be processed by one or more processing entities (e.g.,virtual machines) included in the blockchain layer 216. The write set228 may include changes to key values. The physical infrastructure 214may be utilized to retrieve any of the data or information describedherein.

Within chaincode, a smart contract may be created via a high-levelapplication and programming language, and then written to a block in theblockchain. The smart contract may include executable code which isregistered, stored, and/or replicated with a blockchain (e.g.,distributed network of blockchain peers). A transaction is an executionof the smart contract code which can be performed in response toconditions associated with the smart contract being satisfied. Theexecuting of the smart contract may trigger a trusted modification(s) toa state of a digital blockchain ledger. The modification(s) to theblockchain ledger caused by the smart contract execution may beautomatically replicated throughout the distributed network ofblockchain peers through one or more consensus protocols.

The smart contract may write data to the blockchain in the format ofkey-value pairs. Furthermore, the smart contract code can read thevalues stored in a blockchain and use them in application operations.The smart contract code can write the output of various logic operationsinto the blockchain. The code may be used to create a temporary datastructure in a virtual machine or other computing platform. Data writtento the blockchain can be public and/or can be encrypted and maintainedas private. The temporary data that is used/generated by the smartcontract is held in memory by the supplied execution environment, thendeleted once the data needed for the blockchain is identified.

A chaincode may include the code interpretation of a smart contract,with additional features. As described herein, the chaincode may beprogram code deployed on a computing network, where it is executed andvalidated by chain validators together during a consensus process. Thechaincode receives a hash and retrieves from the blockchain a hashassociated with the data template created by use of a previously storedfeature extractor. If the hashes of the hash identifier and the hashcreated from the stored identifier template data match, then thechaincode sends an authorization key to the requested service. Thechaincode may write to the blockchain the data associated with thecryptographic details.

FIG. 2B illustrates an example of a communication sequence 250 betweennodes of a blockchain network, according to example embodiments. In theexample of FIG. 2B, a client node 260 performs computations to generatesimulated data which is stored in frames. Furthermore, the frames ofsimulated data are distributed among a plurality of endorsing peer nodes271, 272, and 273. When the frames are validated by the endorsing peernodes 271, 272, and 273, the frames are transferred to an ordering node274 which orders the endorsed frames into one or more blocks andtransmits the ordered blocks to nodes of the blockchain network forstorage on a distributed ledger replicated across the nodes.

Referring to FIG. 2B, in 281, the client node 260 transmits a firstframe N to endorsing peer node 271 which is included in a first subsetof endorsing peer nodes. Likewise, in 282 the client node 260 transmitsa second frame N+1 to endorsing peer node 272 which is included in asecond subset of endorsing peer nodes. Furthermore, in 283, the clientnode 260 transmits a third frame N+2 to endorsing peer node 273. Theframes N, N+1, N+2, and the like, may be generated by the client node260 or another system which provides the simulation data to the clientnode 260. Each frame may include a state of the model/data beingsimulated at each step or stage represented by each frame from amongframes N, N+1, and N+2. In response, the client node 260 may identifycheckpoints within the simulation data, compress the data, and generatethe data frames N, N+1, N+2, etc. based on the identified checkpoints.

In 284, the endorsing peer node 271 attempts to validate the frame N.Likewise, in 285 the endorsing peer node 272 attempts to validate frameN+1, and in 286, the endorsing peer node 273 attempts to validate theframe N+2. Here, the validations in 284, 285, and 286 may be performedin parallel. To perform the endorsement process, each endorsing peernode 271-273 may identify a previous validated state of the simulation,recompute the simulation to generate a local state of the model, andcompare the locally computed state to the state included in therespective frame received from the client node 260. In response to thecomputed state being within an acceptable range of deviation with thereceived state, the endorsing peer node determines the frame is valid,and endorses the frame.

In this example, endorsing peer node 272 and endorsing peer node 273determine that frames N+1 and N+2 are valid, respectively. Accordingly,in 289 and 290 the endorsing peer nodes 272 and 273 transmitendorsements to the ordering node 274. Meanwhile, endorsing peer node271 determines that frame N is not valid based on its local computationof state of model at a checkpoint corresponding to frame N. Here, theendorsing peer node 271 determines that a difference between the locallycomputed state and the received state included in frame N differ by morethan an acceptable range of deviation. Accordingly, in 288, theendorsing peer node 271 rejects the state report and sends notice to theclient node 260. In response, the client node 260 may refine thesimulation without re-computation, in some cases, or it may recomputethe state of the simulation. In 289, the client node 260 resubmits theframe N to the endorsing peer node 271 based on the updated/refinedstate. In response, in 291, the endorsing peer node 271 attempts tovalidate the updated state received in 289, and determines to validatethe frame N. Accordingly, in 292, the endorsing peer node 271 transmitsan endorsement of frame N to the ordering node 274.

In response to receiving endorsements of all successive frames N, N+1,N+2, etc., the ordering node 274 orders the frames based on timestampsof the original frame. For example, the ordering node 274 may store theframes in a queue, or the like. Furthermore, the ordered frame dataincluding frames N, N+1, and N+2, may be stored in a data block in 293,and transmitted to the endorsing nodes 271, 272, and 273 in 294 suchthat the data block including the ordered frame data can be committed toa distributed ledger, in 295. Here, the data block may be committed to areplicated copy of the distributed ledger (e.g., blockchain, world stateDB, etc.) that is distributed and shared among the endorsing peer nodes271-273 (and possibly other nodes such as client node 260, ordering node274, and other nodes not shown).

FIG. 3 illustrates an example of a permissioned blockchain network 300,which features a distributed, decentralized peer-to-peer architecture,and a certificate authority 318 managing user roles and permissions. Inthis example, the blockchain user 302 may submit a transaction to thepermissioned blockchain network 310. In this example, the transactioncan be a deploy, invoke or query, and may be issued through aclient-side application leveraging an SDK, directly through a REST API,or the like. Trusted business networks may provide access to regulatorsystems 314, such as auditors (the Securities and Exchange Commission ina U.S. equities market, for example). Meanwhile, a blockchain networkoperator system of nodes 308 manage member permissions, such asenrolling the regulator system 310 as an “auditor” and the blockchainuser 302 as a “client.” An auditor could be restricted only to queryingthe ledger whereas a client could be authorized to deploy, invoke, andquery certain types of chaincode.

A blockchain developer system 316 writes chaincode and client-sideapplications. The blockchain developer system 316 can deploy chaincodedirectly to the network through a REST interface. To include credentialsfrom a traditional data source 330 in chaincode, the developer system316 could use an out-of-band connection to access the data. In thisexample, the blockchain user 302 connects to the network through a peernode 312. Before proceeding with any transactions, the peer node 312retrieves the user's enrollment and transaction certificates from thecertificate authority 318. In some cases, blockchain users must possessthese digital certificates in order to transact on the permissionedblockchain network 310. Meanwhile, a user attempting to drive chaincodemay be required to verify their credentials on the traditional datasource 330. To confirm the user's authorization, chaincode can use anout-of-band connection to this data through a traditional processingplatform 320.

FIG. 4A illustrates a process 400A of a client node 410 generatingframes 412 of a state of a simulation, according to example embodiments.Referring to FIG. 4A, the client node 410 may execute a deep learningmodel, a large-scale non-iterative experiment, or the like, whichgenerates multiple successive frames 412. Each frame 412 may begenerated based on checkpoints 413 identified by the client node 410within the simulated data. Furthermore, the frames 412 may be compressedto generate compressed data frames 414.

According to various embodiments, the simulation may be decomposed intosimpler, easy to validate steps/iterations, which can be integral toperforming the distributed validation across peers in the network. Inparticular, the decomposition allows multiple frames to be validated inparallel across independent endorsers, allowing for a reduction of thevalidation time requirement. Because the decomposition allows for thevalidation to be performed along with the simulation process, not onlyare simultaneous validations of the computations performed, but alsotrust guarantees or points of trust are created that are transferable toa later point in time when the simulations might be referenced forendorsement of subsequent frames.

A sequence of steps of state data of a model may be grouped togetherinto a frame and appended to the blockchain. However, this does notnecessarily limit the implementation to add a separate block for eachframe. One could use ideas such as Merkle tree structures to groupmultiple frames within a single block without any loss of generality, aslong as the frames are added in sequence (of iterations). Similar tovideo compression, the frames of simulation data may be compressed usingdelta encoding, successive refinement, and/or vector quantization toreduce the amount of data needed for storage and overhead for performingcommunication. The simulation or computation may be iterative ornon-iterative. For example, the iterative case accounts for a simple wayto construct frames and perform the delta encoding. Meanwhile, in thecase of non-iterative simulations, a minimum spanning tree-based policymay be used to group states according to closeness, thereby allowing forefficient compression.

According to various embodiments, the checkpoint 413 may be generated ordetermined under multiple different scenarios. For example, if a frameincludes a preset maximum number of states, then a new frame may becreated (with the first state being a checkpoint) so as to ensure thatthe each endorser at most validates a fixed number of states in eachframe. As another example, a checkpoint may be created when successivestates deviate by a magnitude exceeding a chosen maximum value. That is,when the states differ by a significant extent, a checkpoint is created.

After generating the compressed frames 414, the individual compressedframes 414 may be distributed among multiple subsets of endorsing peernodes for validation. Here, a subset of endorsing peer nodes may includeat least one endorsing peer node. By distributing endorsement acrossmultiple subsets, the endorsement process (including decompression,re-computation, deviation determination, etc.) may be performed inparallel thereby significantly reducing processing time. Furthermore,each endorsing node does not have to compute/validate every frame.Rather, the endorsing nodes may only endorse a fraction of the frameswhile retrieving trusted validation data from the blockchain.

FIG. 4B illustrates a process 400B of an endorsing node 420 validating acompressed frame 422 of simulation data, according to exampleembodiments. In this example, the frame of compressed data may includestate information of a simulation. The state is the intermediate valuesthat are needed to continue the iterations of a computation. In the caseof an epidemiological simulation, for example, the state may include aset of values that describe the disease spread. In the case of traininga deep neural network, for example, the state may be a set of allweights applied to nodes of a neural network, and the like. In someembodiments, the training data may have already been transmitted to allpeers in advance.

During an endorsement process 424, each endorsing peer node maydecompress the frame 422 to reveal the state data of the simulationprovided from the client. The endorsing peer node 420 may retrieve aprevious state of the simulation from the blockchain. Here, the previousstate may be the most recent state that has been successfully validatedand committed to the blockchain which may be stored locally on theendorsing peer node 420. The endorsing peer node 420 may recompute thecurrent state of the simulation which corresponds to the received statein the compressed frame 422 and determine whether the locally generatedstate is similar enough to the state in the compressed frame 422 tovalidate the frame 422 for endorsement. If the state values differ bymore than a predetermined threshold, the endorsing peer node 420 maytransmit notification to the client node indicating the endorsement isdeclined. In response, the client node may refine the state of the frame422 and resubmit the frame 422 after updating the state based on therefinement. This process can be repeated until the frame 422 isvalidated. Meanwhile, once validated, the validated frame 426 may besubmitted to an ordering node for inclusion on the blockchain.

FIG. 4C illustrates a process 400C of an ordering node 430 arrangingframes of simulation data in blocks, according to example embodiments.Referring to FIG. 4C, a plurality of frames are validated by a pluralityof subsets of endorsing peers and provided to the ordering node 430. Inresponse, the ordering node 430 may arrange the frames in a sequentialorder (or successive order such as 1, 2, 3, etc.) within a queue 432based on a timestamp or other information within the frames added whenthe frame was generated by the client. Furthermore, the arranged framesmay be ordered within one or more data blocks 434 which may betransmitted to nodes of the blockchain network and committed to thedistributed ledger shared among the nodes.

FIG. 5A illustrates a method 510 of generating frames of simulation datafor storage on a blockchain, according to example embodiments. Forexample, the method 510 may be performed by a peer node of a blockchainsuch as a client node, or the like. As another example, the method maybe performed by any computing device with a processor and a storage suchas a server, a database, a cloud platform, multiple devices, and thelike. Referring to FIG. 5A, in 511, the method may include obtainingdata of a simulation. For example, a simulation such as an iterativecomputation or a non-iterative computation may be performed and maygenerate simulation data. In some embodiments, the simulation may beperformed by another device which transmits the simulation data to thepeer node, etc.

In 512, the method may include identifying checkpoints within thesimulation data, and in 513, the method may include generating aplurality of successive frames of the simulation data based on theidentified checkpoints, where each frame identifies an evolving state ofthe simulation with respect to a previous frame among the successiveframes. For example, the checkpoints may be identified based oniterations of the simulation, input/output pairs of a non-iterativesimulation, and the like. For example, if a frame of simulation dataincludes a preset maximum number of states, then a new frame is created(with the first state being a checkpoint) so as to ensure that eachendorser at most validates a fixed number of states in each frame. Asanother example, a checkpoint may be created when successive statesdeviate by a magnitude exceeding a chosen maximum value. That is, whenthe states differ by a significant extent, a checkpoint is created. Thecheckpointing may be used to identify an end of a previous frame and astart of a next successive frame within the computational data. In someembodiments, the frames may further be compressed based on a type ofcomputation (e.g., iterative, non-iterative, or the like).

In some embodiments, a width of each frame may be adaptively determinedbased on a frequency of checkpoints that correspond to the respectiveframe within the received data of the simulation. In some embodiments,the simulation may include one of an iterative simulation of a modelbeing trained where each iteration further refines a state of the modelbased on one or more algorithms, neural networks, and the like. Asanother example, a simulation may include a non-iterative simulationwhich includes a set of input and output pairs being processed.

In 514, the method may include transmitting the generated successiveframes to nodes of a blockchain network for inclusion in one or moredata blocks within a hash-linked chain of data blocks. For example, eachframe may be transmitted to different endorsing peer groups or subsetsof nodes within the blockchain network. In some embodiments, the methodmay further include storing an iteration ID within each frame from amongthe successive generated successive frames, wherein the iteration IDidentifies a respective iteration of the iterative simulation associatedwith the respective frame. In some embodiments, the method may furtherinclude receiving a message from a blockchain peer node indicating astate associated with a frame from among the generated successive frameshas been invalidated. In response, the method may further include one ormore of recomputing the state and refining the state of the invalidatedframe to generate an updated frame and transmitting the updated frame tothe blockchain peer node for validation.

FIG. 5B illustrates a method 520 of endorsing a frame of simulationdata, according to example embodiments. For example, the method 520 maybe performed by one or more endorsing nodes of a blockchain network. Thenode may include a computing system such as a server, a database, acloud platform, or the like. Referring to FIG. 5B, in 521, the methodmay include receiving a data frame from among successive data frames ofan iterative simulation. Here, the data frame may state data of theiterative simulation which includes an identification of the currentstate of the model (the algorithm, etc.) the training data, and thelike. In some embodiments, the data frame may be compressed using acompression scheme. In some embodiments, the data frame may bedecompressed to reveal the state of the simulation before thecompression.

In 522, the method may include generating a local computation of statedata for the data frame based on previously validated state data from aprevious frame. Here, the previously validated state data may be storedin a previous frame within a block of the blockchain. In 523, the methodmay include determining a similarity between the local computation ofstate data and the state data within the received data frame. Inresponse to a determination that the similarity is within apredetermined threshold, in 524 the method may include transmitting anendorsement of the data frame to a blockchain network for inclusionwithin a data block among a hash-linked chain of data blocks.

In some embodiments, the iterative simulation may include a deeplearning simulation of a model being trained via a neural network, andthe like. Furthermore, the successive frames store changes in state ofthe model over iterations. In some embodiments, the predeterminedthreshold may identify an acceptable level of deviation between thelocal computation of the state data and the state data within thereceived data frame. In some embodiments, the method may furtherinclude, in response to a determination that the similarity is outsidethe predetermined threshold, transmitting a message to a client nodethat submitted the data frame indicating the state data is invalid. Insome embodiments, the method may further include receiving an updateddata frame including a refined state data and determining a similaritybetween the location computation of the state data and the refined statedata within the updated data frame.

FIG. 5C illustrates a method 530 of assigning successive frames ofsimulation data for parallel endorsement, according to exampleembodiments. For example, the method 530 may be performed by a clientnode of a blockchain network. The node may include a computing systemsuch as a server, a database, a cloud platform, or the like. Referringto FIG. 5C, in 531, the method may include generating a plurality ofsuccessive data points of an iterative simulation based on predefinedcheckpoints. For example, each data point may include a frame or windowof data that stores an identification of an evolving state of theiterative simulation with respect to a previous data point among thesuccessive data points. Here, the simulation may be a deep learningsimulation such as a machine learning algorithm being trained via aneural network or the like. The data point may be compressed to reducecomputational overhead.

In 532, the method may include transmitting a blockchain request forvalidating state data within a first data point among the plurality ofsuccessive data points to a first subset of endorsing nodes of ablockchain network, and in 532, the method may further includetransmitting a blockchain request for validating state data within asecond data point among the plurality of successive data points to asecond subset of endorsing nodes which are mutually exclusive from thefirst subset of endorsing nodes of the blockchain network for parallelendorsement of the first and second data points. The client node mayreduce computation of endorsing peer nodes by using only a subset ofendorsing peer nodes for endorsing a frame and using a different subsetof endorsing nodes for endorsing a different frame.

In some embodiments, the state data of the first data point is generatedbased on a first iteration of the iterative simulation and the statedata of the second data point is based on a subsequent iteration of theiterative simulation. In some embodiments, the second data point storesdifferences between a state of the iterative simulation of the firstdata point and a state of the iterative simulation at the second datapoint. In some embodiments, the method further include receiving amessage from the second subset of endorsing nodes indicating the statedata of the second data point is invalid.

In some embodiments, the method may further include refining the statedata of the invalidated second data point to generate an updated statedata for the second data point and transmitting the updated state datato the second subset of endorsing nodes for validation. In someembodiments, the method may further include storing an iteration IDwithin each data point from among the successive data points, whereinthe iteration ID identifies a respective iteration of the iterativesimulation associated with the respective data point. In someembodiments, the method may further include executing the iterativesimulation to generate the plurality of successive data points.

FIG. 5D illustrates a method 540 of ordering simulation data as a resultof parallel endorsement processing, according to example embodiments.For example, the method 540 may be performed by an ordering node of ablockchain network. The node may include a computing system such as aserver, a database, a cloud platform, or the like. Referring to FIG. 5D,in 541, the method may include receiving, via a first subset of peernodes, a verification of state data of a first data point among aplurality of successive data points generated by an iterativesimulation, and in 542, receiving, via a second subset of endorsing peernodes which are mutually exclusive from the first subset of peer nodes,a verification of state data of a second data point among the pluralityof successive data points of the iterative simulation. Here, the firstand second data points may be verified or otherwise endorsed, inparallel, by different subsets of endorsing peer nodes of a sameblockchain network including the ordering node.

In 532, the method may include generating one or more data blocks whichinclude the first and second data points including the validated statedata, and in 533, the method may include transmitting the one or moredata blocks to peer nodes within a blockchain network for storage amonga hash-linked chain of data blocks. For example, the ordering node maytransmit the data blocks to peer nodes in the blockchain network forstorage on a local replica of the blockchain held by each peer node.

In some embodiments, the method may further include arranging the firstdata point and the second data point within a queue based on timestampsincluded in the first data point and the second data point. In thisexample, the generating the one or more data blocks may include orderingthe first and second data points within the one or more data blocksbased on a position of the first and second data points within thequeue. In some embodiments, each verification may indicate that thestate data of the respective data point is within an acceptable range ofdeviation from a predefined threshold. In some embodiments, the firstand second data points may each comprise an iteration ID that identifiesa respective iteration of the iterative simulation associated with therespective data point.

FIG. 5E illustrates a method 550 of compressing simulation content,according to example embodiments. For example, the method 550 may beperformed by client node of a blockchain network which generates and/orreceives computational data. The node may include a computing systemsuch as a server, a database, a cloud platform, multiple systems, or thelike. Referring to FIG. 5E, in 551 the method may include generating adata frame storing content of a simulation. Here, the data frame may beone of multiple data frames including successively generated state dataof a computational model and data such as a deep learning model, alarge-scale simulation, and the like.

In 552, the method may include compressing the simulation content withinthe data frame based on previous simulation content stored in anotherdata frame to generate a compressed data frame. For example, thesimulation content may be state data from an iterative simulation orstate data from a non-iterative simulation. If the simulation is anon-iterative simulation having a plurality of input/output pairs, thecompressing may include compressing the non-iterative simulation contentbased on a minimum spanning tree (MST) process that is based on acloseness of input/output pairs. As another example, if the simulationis an iterative simulation having a state that iteratively evolves, thecompressing may include compressing the iterative simulation contentbased on differences in state with a previous iteration of the iterativesimulation. The previous state may be identified by the node and used toinsert deltas of the state data between the two frames, rather thaninclude the entire state data.

In 553, the method may include transmitting the compressed data framevia a blockchain request to one or more endorsing peer nodes of ablockchain network for inclusion of the compressed data frame within ahash-linked chain of blocks of the blockchain network. For example, theblockchain request may include a request to endorse the transaction forstorage on a blockchain. In some embodiments, the data frame may includean adaptive size based on one or more predefined checkpoints within thesimulation content. In some embodiments, the compressed data frame mayinclude a state update with respect to a previous compressed data frameof the simulation content.

FIG. 5F illustrates a method 560 of endorsing compressed simulationcontent, according to example embodiments. For example, the method 560may be performed by an endorsing peer node or subset of endorsing peernodes within a blockchain network. Referring to FIG. 5F, in 561, themethod may include receiving a data frame that includes simulationcontent which has been compressed based on previous simulation contentstored in a previous data frame. In some embodiments, the compressedcontent may be decompressed. In 562, the method may include extractingthe previous simulation content stored in the previous data frame from ahash-linked chain of data blocks. The previous simulation content may beextracted from a blockchain which stores previously validated state dataof the simulation in blocks.

In 563, the method may include generating local simulation content basedon the extracted previous simulation content. Here, the local simulationcontent may be generated by recomputing the state value of thesimulation using the previously validated state of the simulation storedby the node, to determine an estimate of the state. In 564, the methodmay include determining whether to endorse the received data frame forinclusion in the hash-linked chain of blocks based on a differencebetween the received compressed simulation content and the localsimulation content. The determining may be based on a similarity betweenthe received state data of the simulation content and state data thelocally generated simulation content. If the state data is within apredefined threshold deviation, the endorsement may be authorized. In565, in response to a determination to endorse the received data frame,the method may include transmitting an endorsement to a peer node in ablockchain network.

In some embodiments, the received compressed simulation content may bestored in the data frame and may include content from a non-iterativesimulation having a plurality of input/output pairs. In this example,the non-iterative simulation content may be compressed based on an MSTprocess that is based on a closeness of input/output pairs. In someembodiments, the received compressed simulation content may be stored inthe data frame and may include content from an iterative simulationhaving a state that iteratively evolves. In some embodiments, theiterative simulation content may be compressed based on differences instate with a previous iteration of the iterative simulation.

FIG. 6A illustrates an example physical infrastructure configured toperform various operations on the blockchain in accordance with one ormore of the example methods of operation according to exampleembodiments. Referring to FIG. 6A, the example configuration 600Aincludes a physical infrastructure 610 with a blockchain 620 and a smartcontract 640, which may execute any of the operational steps 612included in any of the example embodiments. The steps/operations 612 mayinclude one or more of the steps described or depicted in one or moreflow diagrams and/or logic diagrams. The steps may represent output orwritten information that is written or read from one or more smartcontracts 640 and/or blockchains 620 that reside on the physicalinfrastructure 610 of a computer system configuration. The data can beoutput from an executed smart contract 640 and/or blockchain 620. Thephysical infrastructure 610 may include one or more computers, servers,processors, memories, and/or wireless communication devices.

FIG. 6B illustrates an example smart contract configuration amongcontracting parties and a mediating server configured to enforce thesmart contract terms on the blockchain according to example embodiments.Referring to FIG. 6B, the configuration 650B may represent acommunication session, an asset transfer session or a process orprocedure that is driven by a smart contract 630 which explicitlyidentifies one or more user devices 652 and/or 656. The execution,operations and results of the smart contract execution may be managed bya server 654. Content of the smart contract 640 may require digitalsignatures by one or more of the entities 652 and 656 which are partiesto the smart contract transaction.

FIG. 6C illustrates an example smart contract configuration amongcontracting parties and a mediating server configured to enforce thesmart contract terms on the blockchain according to example embodiments.Referring to FIG. 6C, the configuration 650 may represent acommunication session, an asset transfer session or a process orprocedure that is driven by a smart contract 630 which explicitlyidentifies one or more user devices 652 and/or 656. The execution,operations and results of the smart contract execution may be managed bya server 654. Content of the smart contract 630 may require digitalsignatures by one or more of the entities 652 and 656 which are partiesto the smart contract transaction. The results of the smart contractexecution may be written to a blockchain 620 as a blockchaintransaction. In this example, the smart contract 630 resides on theblockchain 620 which may reside on one or more computers, servers,processors, memories, and/or wireless communication devices.

FIG. 6D illustrates a common interface for accessing logic and data of ablockchain, according to example embodiments. Referring to the exampleof FIG. 6D, an application programming interface (API) gateway 662provides a common interface for accessing blockchain logic (e.g., smartcontract 630 or other chaincode) and data (e.g., distributed ledger,etc.) In this example, the API gateway 662 is a common interface forperforming transactions (invoke, queries, etc.) on the blockchain byconnecting one or more entities 652 and 656 to a blockchain peer (i.e.,server 654). The server 654 is a blockchain network peer component thatholds a copy of the world state and a distributed ledger allowingclients 652 and 656 to query data on the world state as well as submittransactions into the blockchain network where, depending on the smartcontract 630 and endorsement policy, endorsing peers will run the smartcontracts 630.

FIG. 7A illustrates a process 700 of a new block 730 being added to adistributed ledger 720, according to example embodiments, and FIG. 7Billustrates contents of a block structure 730 for blockchain, accordingto example embodiments. Referring to FIG. 7A, clients (not shown) maysubmit transactions to blockchain nodes 711, 712, and/or 713. Clientsmay be instructions received from any source to enact activity on theblockchain. As an example, clients may be applications (based on a SDK)that act on behalf of a requester, such as a device, person or entity topropose transactions for the blockchain. The plurality of blockchainpeers (e.g., blockchain nodes 711, 712, and 713) may maintain a state ofthe blockchain network and a copy of the distributed ledger 720.

Different types of blockchain nodes/peers may be present in theblockchain network including endorsing peers which simulate and endorsetransactions proposed by clients and committing peers which verifyendorsements, validate transactions, and commit transactions to thedistributed ledger 720. In this example, the blockchain nodes 711, 712,and 713 may perform the role of endorser node, committer node, or both.

The distributed ledger 720 includes a blockchain 722 which storesimmutable, sequenced records in blocks, and a state database 724(current world state) maintaining a current state (key values) of theblockchain 722. One distributed ledger 720 may exist per channel andeach peer maintains its own copy of the distributed ledger 720 for eachchannel of which they are a member. The blockchain 722 is a transactionlog, structured as hash-linked blocks where each block contains asequence of N transactions. Blocks (e.g., block 730) may include variouscomponents such as shown in FIG. 7B. The linking of the blocks (shown byarrows in FIG. 7A) may be generated by adding a hash of a prior block'sheader within a block header of a current block. In this way, alltransactions on the blockchain 722 are sequenced and cryptographicallylinked together preventing tampering with blockchain data withoutbreaking the hash links. Furthermore, because of the links, the latestblock in the blockchain 722 represents every transaction that has comebefore it. The blockchain 722 may be stored on a peer file system (localor attached storage), which supports an append-only blockchain workload.

The current state of the blockchain 722 and the distributed ledger 720may be stored in the state database 724. Here, the current state datarepresents the latest values for all keys ever included in the chaintransaction log of the blockchain 722. Chaincode invocations executetransactions against the current state in the state database 724. Tomake these chaincode interactions efficient, the latest values of allkeys may be stored in the state database 724. The state database 724 mayinclude an indexed view into the transaction log of the blockchain 722and can therefore be regenerated from the chain at any time. The statedatabase 724 may automatically get recovered (or generated if needed)upon peer startup, before transactions are accepted.

Endorsing nodes receive transactions from clients and endorse thetransaction (e.g., changes in simulation state, etc.) based on simulatedresults. Endorsing nodes may hold smart contracts which simulate thetransaction proposals. The nodes needed to endorse a transaction dependson an endorsement policy which may be specified within chaincode. Anexample of an endorsement policy is “the majority of endorsing peersmust endorse the transaction.” Different channels may have differentendorsement policies. Endorsed transactions are forward by the clientapplication to an ordering service 710.

The ordering service 710 accepts endorsed transactions, orders them intoa block, and delivers the blocks to the committing peers. For example,the ordering service 710 may initiate a new block when a threshold oftransactions has been reached, a timer times out, or another condition.The ordering service 710 may operate based on the timestamp agreementprocesses described herein such as calculating a final timestamp foreach transaction based on a weighted average of timestamps fromblockchain nodes 711-713, etc. In the example of FIG. 7A, blockchainnode 712 is a committing peer that has received a new data block 730 forstorage on blockchain 722.

The ordering service 710 may be made up of a cluster of orderers. Theordering service 710 does not process transactions, smart contracts, ormaintain the shared ledger. Rather, the ordering service 710 may acceptthe endorsed transactions, determine a final timestamp for transactions,and specifies the order in which those transactions are committed to thedistributed ledger 720 based on the final timestamps. The architectureof the blockchain network may be designed such that the specificimplementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.) becomes apluggable component.

Transactions are written to the distributed ledger 720 in a consistentorder. The order of transactions is established to ensure that theupdates to the state database 724 are valid when they are committed tothe network. Unlike a cryptocurrency blockchain system (e.g., Bitcoin,etc.) where ordering occurs through the solving of a cryptographicpuzzle, or mining, in this example the parties of the distributed ledger720 may choose the ordering mechanism that best suits that network suchas chronological ordering.

When the ordering service 710 initializes a new block 730, the new block730 may be broadcast to committing peers (e.g., blockchain nodes 711,712, and 713). In response, each committing peer validates thetransaction within the new block 730 by checking to make sure that theread set and the write set still match the current world state in thestate database 724. Specifically, the committing peer can determinewhether the read data that existed when the endorsers simulated thetransaction is identical to the current world state in the statedatabase 724. When the committing peer validates the transaction, thetransaction is written to the blockchain 722 on the distributed ledger720, and the state database 724 is updated with the write data from theread-write set. If a transaction fails, that is, if the committing peerfinds that the read-write set does not match the current world state inthe state database 724, the transaction ordered into a block will stillbe included in that block, but it will be marked as invalid, and thestate database 724 will not be updated.

Referring to FIG. 7B, a block 730 (also referred to as a data block)that is stored on the blockchain 722 of the distributed ledger 720 mayinclude multiple data segments such as a block header 732, block data734, and block metadata 736. It should be appreciated that the variousdepicted blocks and their contents, such as block 730 and its contentsshown in FIG. 7B are merely for purposes of example and are not meant tolimit the scope of the example embodiments. In some cases, both theblock header 732 and the block metadata 736 may be smaller than theblock data 734 which stores transaction data, however this is not arequirement. The block 730 may store transactional information of Ntransactions (e.g., 100, 500, 1000, 2000, 3000, etc.) within the blockdata 734. According to various embodiments, each transaction may includeframe data 735 which includes changes in state of a simulation (e.g.,iterative, non-iterative, etc.) which have been validated by endorsingpeer nodes.

The metadata 736 may include an identification of a client node/workerthat generated the data frame, the endorsing nodes that endorse theframe, an iteration(s) of the simulation where the state data is takenfrom (e.g., iterate index, etc.), an environment ID pointing to aninstance of simulation that was run to generate the frame, and the like.The metadata may be stored in the block metadata section 736 in additionto valid/invalid indicator for every transaction. The metadata stored isuseful to revalidate the indicator of the information stored in the datablock.

Traditional blockchains store information such as transactions (transferof property), and smart contracts (instruction sets to be executed). Inthe present embodiments, the data being stored may also include thevalidated states of the computational process. That is, each block mayinclude a frame that includes the compressed checkpoint and quantizedstate updates obtained from delta encoding of successive iterates in aframe. Additional metadata to be stored might include the ID of theclient performing the simulation, the endorsers who validated thestates, the iterate index, and other extraneous information used forperforming the simulation.

The block 730 may also include a link to a previous block (e.g., on theblockchain 722 in FIG. 7A) within the block header 732. In particular,the block header 732 may include a hash of a previous block's header.The block header 732 may also include a unique block number, a hash ofthe block data 734 of the current block 730, and the like. The blocknumber of the block 730 may be unique and assigned in anincremental/sequential order starting from zero. The first block in theblockchain may be referred to as a genesis block which includesinformation about the blockchain, its members, the data stored therein,etc.

The block data 734 may store transactional information of eachtransaction that is recorded within the block 730. For example, thetransaction data stored within block data 734 may include one or more ofa type of the transaction, a version, a timestamp (e.g., finalcalculated timestamp, etc.), a channel ID of the distributed ledger 720,a transaction ID, an epoch, a payload visibility, a chaincode path(deploy tx), a chaincode name, a chaincode version, input (chaincode andfunctions), a client (creator) identify such as a public key andcertificate, a signature of the client, identities of endorsers,endorser signatures, a proposal hash, chaincode events, response status,namespace, a read set (list of key and version read by the transaction,etc.), a write set (list of key and value, etc.), a start key, an endkey, a list of keys, a Merkel tree query summary, and the like. Thetransaction data may be stored for each of the N transactions.

The block metadata 736 may store multiple fields of metadata (e.g., as abyte array, etc.). Metadata fields may include signature on blockcreation, a reference to a last configuration block, a transactionfilter identifying valid and invalid transactions within the block, lastoffset persisted of an ordering service that ordered the block, and thelike. The signature, the last configuration block, and the orderermetadata may be added by the ordering service 710. Meanwhile, acommitting node of the block (such as blockchain node 712) may addvalidity/invalidity information based on an endorsement policy,verification of read/write sets, and the like. The transaction filtermay include a byte array of a size equal to the number of transactionsin the block data 734 and a validation code identifying whether atransaction was valid/invalid.

The above embodiments may be implemented in hardware, in a computerprogram executed by a processor, in firmware, or in a combination of theabove. A computer program may be embodied on a computer readable medium,such as a storage medium. For example, a computer program may reside inrandom access memory (“RAM”), flash memory, read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), registers, hard disk, aremovable disk, a compact disk read-only memory (“CD-ROM”), or any otherform of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such thatthe processor may read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anapplication specific integrated circuit (“ASIC”). In the alternative,the processor and the storage medium may reside as discrete components.For example, FIG. 8 illustrates an example computer system architecture800, which may represent or be integrated in any of the above-describedcomponents, etc.

FIG. 8 is not intended to suggest any limitation as to the scope of useor functionality of embodiments of the application described herein.Regardless, the computing node 800 is capable of being implementedand/or performing any of the functionality set forth hereinabove. Forexample, the computing node 800 may perform any of the methods 510-560shown and described with respect to FIGS. 5A-5F.

In computing node 800 there is a computer system/server 802, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 802 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 802 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 802 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 8 , computer system/server 802 in cloud computing node800 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 802 may include, but are notlimited to, one or more processors or processing units 804, a systemmemory 806, and a bus that couples various system components includingsystem memory 806 to processor 804.

The bus represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 802 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 802, and it includes both volatileand non-volatile media, removable and non-removable media. System memory806, in one embodiment, implements the flow diagrams of the otherfigures. The system memory 806 can include computer system readablemedia in the form of volatile memory, such as random-access memory (RAM)810 and/or cache memory 812. Computer system/server 802 may furtherinclude other removable/non-removable, volatile/non-volatile computersystem storage media. By way of example only, storage system 814 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus by one or more data media interfaces. As will be further depictedand described below, memory 806 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of various embodiments of the application.

Program/utility 816, having a set (at least one) of program modules 818,may be stored in memory 806 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 818 generally carry out the functionsand/or methodologies of various embodiments of the application asdescribed herein.

As will be appreciated by one skilled in the art, aspects of the presentapplication may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present application may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present application may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Computer system/server 802 may also communicate with one or moreexternal devices 820 such as a keyboard, a pointing device, a display822, etc.; one or more devices that enable a user to interact withcomputer system/server 802; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 802 to communicate withone or more other computing devices. Such communication can occur viaI/O interfaces 824. Still yet, computer system/server 802 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 826. As depicted, network adapter 826communicates with the other components of computer system/server 802 viaa bus. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 802. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Although an exemplary embodiment of at least one of a system, method,and non-transitory computer readable medium has been illustrated in theaccompanied drawings and described in the foregoing detaileddescription, it will be understood that the application is not limitedto the embodiments disclosed, but is capable of numerous rearrangements,modifications, and substitutions as set forth and defined by thefollowing claims. For example, the capabilities of the system of thevarious figures can be performed by one or more of the modules orcomponents described herein or in a distributed architecture and mayinclude a transmitter, receiver or pair of both. For example, all orpart of the functionality performed by the individual modules, may beperformed by one or more of these modules. Further, the functionalitydescribed herein may be performed at various times and in relation tovarious events, internal or external to the modules or components. Also,the information sent between various modules can be sent between themodules via at least one of: a data network, the Internet, a voicenetwork, an Internet Protocol network, a wireless device, a wired deviceand/or via plurality of protocols. Also, the messages sent or receivedby any of the modules may be sent or received directly and/or via one ormore of the other modules.

One skilled in the art will appreciate that a “system” could be embodiedas a personal computer, a server, a console, a personal digitalassistant (PDA), a cell phone, a tablet computing device, a smartphoneor any other suitable computing device, or combination of devices.Presenting the above-described functions as being performed by a“system” is not intended to limit the scope of the present applicationin any way but is intended to provide one example of many embodiments.Indeed, methods, systems and apparatuses disclosed herein may beimplemented in localized and distributed forms consistent with computingtechnology.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge-scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether but may comprise disparate instructions stored in differentlocations which, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, random access memory (RAM), tape, or any othersuch medium used to store data.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

It will be readily understood that the components of the application, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations.Thus, the detailed description of the embodiments is not intended tolimit the scope of the application as claimed but is merelyrepresentative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that theabove may be practiced with steps in a different order, and/or withhardware elements in configurations that are different than those whichare disclosed. Therefore, although the application has been describedbased upon these preferred embodiments, it would be apparent to those ofskill in the art that certain modifications, variations, and alternativeconstructions would be apparent.

While preferred embodiments of the present application have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the application is to be definedsolely by the appended claims when considered with a full range ofequivalents and modifications (e.g., protocols, hardware devices,software platforms etc.) thereto.

What is claimed is:
 1. A computing system comprising: a networkinterface configured to receive a data structure from among sequentialdata structures of an iterative simulation that store state data of theiterative simulation; and a processor configured to generate a localcomputation of state data for the data structure based on previouslyvalidated state data from a previous data structure, and determine asimilarity between the local computation of state data and the statedata within the received data structure, wherein, in response to adetermination that the similarity is within a predetermined threshold,the processor is further configured to control the network interface totransmit an endorsement of the data structure to a blockchain networkfor inclusion within a data block among a hash-linked chain of datablocks; wherein a similarity is determined between a locationcomputation of the state data and a refined state data within theupdated data structure.
 2. The computing system of claim 1, wherein theiterative simulation comprises a deep learning simulation of a model. 3.The computing system of claim 2, wherein the sequential data structuresstore changes in state of the model over iterations.
 4. The computingsystem of claim 1, wherein the predetermined threshold identifies anacceptable level of deviation between the local computation of the statedata and the state data within the received data structure.
 5. Thecomputing system of claim 1, wherein, in response to a determinationthat the similarity is outside the predetermined threshold, theprocessor is further configured to control the network interface totransmit a message to a client node that submitted the data structurewhich indicates the state data is invalid.
 6. The computing system ofclaim 1, wherein the network interface is further configured to receivean updated data structure that includes the refined state data.
 7. Amethod comprising: receiving a data structure from among sequential datastructures of an iterative simulation, the data structure storing statedata of the iterative simulation; generating a local computation ofstate data for the data structure based on previously validated statedata from a previous data structure; determining a similarity betweenthe local computation of state data and the state data within thereceived data structure; and in response to a determination that thesimilarity is within a predetermined threshold, transmitting anendorsement of the data structure to a blockchain network for inclusionwithin a data block among a hash-linked chain of data blocks; wherein asimilarity is determined between a location computation of the statedata and a refined state data within the updated data structure.
 8. Themethod of claim 7, wherein the iterative simulation comprises a deeplearning simulation of a model being trained.
 9. The method of claim 8,wherein the sequential data structures store changes in state of themodel over iterations.
 10. The method of claim 7, wherein thepredetermined threshold identifies an acceptable level of deviationbetween the local computation of the state data and the state datawithin the received data structure.
 11. The method of claim 7, furthercomprising, in response to a determination that the similarity isoutside the predetermined threshold, transmitting a message to a clientnode that submitted the data structure indicating the state data isinvalid.
 12. The method of claim 7, further comprising receiving anupdated data structure including the refined state data.
 13. Anon-transitory computer readable medium comprising instructions; thatwhen read by a processor, cause the processor to perform a methodcomprising: receiving a data structure from among sequential datastructures of an iterative simulation, the data structure storing statedata of the iterative simulation; generating a local computation ofstate data for the data structure based on previously validated statedata from a previous data structure; determining a similarity betweenthe local computation of state data and the state data within thereceived data structure; and in response to a determination that thesimilarity is within a predetermined threshold, transmitting anendorsement of the data structure to a blockchain network for inclusionwithin a data block among a hash-linked chain of data blocks; wherein asimilarity is determined between a location computation of the statedata and a refined state data within the updated data structure.
 14. Thenon-transitory computer readable medium of claim 13, wherein theiterative simulation comprises a deep learning simulation of a modelbeing trained.
 15. The non-transitory computer readable medium of claim13, wherein the sequential data structures store changes in state of themodel over iterations.
 16. The non-transitory computer readable mediumof claim 13, wherein the predetermined threshold identifies anacceptable level of deviation between the local computation of the statedata and the state data within the received data structure.
 17. Thenon-transitory computer readable medium of claim 13, further comprising,in response to a determination that the similarity is outside thepredetermined threshold, transmitting a message to a client node thatsubmitted the data structure indicating the state data is invalid.